Unless otherwise indicated herein, the approaches described in this section are not admitted to be prior art by inclusion in this section.
Distributed computing platforms allow workloads to be processed across a cluster of distributed software components that are usually supported by multiple computing devices. For example, Hadoop is an open-source framework for distributed storage and distributed processing of very large data sets. Hadoop splits a data set into blocks and distributes the processing workload across nodes in a distributed computing cluster. This distributed approach enables data to be processed in parallel and more efficiently compared to using a single device or single software instance. In practice, a distributed computing platform may be deployed in a virtualized computing environment. Virtualization allows the abstraction of hardware resources and the pooling of these resources to support virtual machines configured to perform distributed processing. However, it may be challenging to support multiple distributed computing clusters in a particular virtualized computing environment.